| Challenge: | Abusive text is a serious problem in social media and causes many issues among users . a model that detects text abusiveness in context without explicit abusive words is challenging . |
| Approach: | They propose to use an abusive lexicon to determine the existence of an abusive word in text . they combine local and global features to evaluate the model using benchmark data . |
| Outcome: | The proposed model outperforms all previous models for detecting abusiveness in text without abusive words. |
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Humans Need Context, What about Machines? Investigating Conversational Context in Abusive Language Detection (2024.lrec-main)
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| Challenge: | In this paper, we examine the role of conversational context in abusive language detection . prior studies have ignored the contextual nature of abusive language, ignoring this aspect . toxicity, hate speech, harmful stereotypes are among the forms of harmful language . |
| Approach: | They propose to use conversational context to analyze abusive language detection using two methods . they use "abusive language" as an umbrella term to refer to various forms of harmful language . |
| Outcome: | The proposed approach is based on two datasets in English and a new dataset of French tweets annotated for hate speech and stereotypes. |
Implicitly Abusive Language – What does it actually look like and why are we not getting there? (2021.naacl-main)
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| Challenge: | Existing datasets make learning implicit abuse difficult, argues a new position paper . a lack of work on implicit abuse has limited the effectiveness of automatic detection . |
| Approach: | They argue that existing datasets make learning implicit abuse difficult . they propose a divide-and-conquer strategy to detect implicit abuse . |
| Outcome: | The proposed model could be improved to detect implicit abuse in a dataset with a standardized model. |
Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition (D19-50)
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| Challenge: | Existing methods to detect abusive language only train one classifier for the whole variety of offending . a new method can support a moderator with explicit unraveled explanations for why something was flagged as abusive . |
| Approach: | a new method is proposed to distinguish explicitly abusive cases from the more "shadowed" ones . the researchers extend a lexicon of abusive terms to include new obfuscations of abusive words . |
| Outcome: | a new method can distinguish explicitly abusive cases from the more "shadowed" ones . the method can support a moderator with explicit unraveled explanations for why something was flagged as abusive . |
Graphically Speaking: Unmasking Abuse in Social Media with Conversation Insights (2025.acl-long)
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| Challenge: | Existing approaches to detect abusive language often ignore conversational context, leading to inconsistent and sometimes inconclusive results. |
| Approach: | They propose a graph neural network approach that uses conversational context to model social media conversations as graphs, where nodes represent comments and edges capture reply structures. |
| Outcome: | The proposed model outperforms baseline and linear context-aware methods and achieves significant improvements in F1 scores. |
Cross-domain and Cross-lingual Abusive Language Detection: A Hybrid Approach with Deep Learning and a Multilingual Lexicon (P19-2)
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| Challenge: | Detecting online abusive language in social media messages is gaining increasing attention from scholars and stakeholders. |
| Approach: | They propose a hybrid approach with deep learning and a multilingual lexicon to cross-domain and cross-lingual detection of abusive content. |
| Outcome: | The proposed system can detect abusive content across domains and languages using a multilingual lexicon and a domain-independent lexical. |
Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors (2022.acl-long)
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| Challenge: | a new study shows that general abusive language classifiers are reliable in detecting explicit abuse but fail to detect more subtle abuses. |
| Approach: | They propose an interpretability technique to quantify the sensitivity of a trained model to new data . they propose a degree of explicitness metric to suggest out-of-domain unlabeled examples . |
| Outcome: | The proposed interpretability technique is useful for predicting the generalizability of the model on new data. |
Abusive language in Spanish children and young teenager’s conversations: data preparation and short text classification with contextual word embeddings (2020.lrec-1)
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| Challenge: | Existing studies on how to automatically detect abusive short texts are gaining interest in the natural language processing community. |
| Approach: | They propose to use a contextual word embedding model to automatically detect abusive short texts for Spanish language. |
| Outcome: | The proposed model outperforms classical methods in the detection of abusive short texts for the spanish language. |
Inducing a Lexicon of Abusive Words – a Feature-Based Approach (N18-1)
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| Challenge: | a new classification task is needed to identify abusive words among a set of negative polar expressions. |
| Approach: | They propose to calibrate a domain-independent lexicon for detection of abusive words . they use a small manually annotated base lexico to calibrated a large lexical . |
| Outcome: | The proposed feature can be calibrated on a small manually annotated base lexicon and produced on large datasets. |
Do You Really Want to Hurt Me? Predicting Abusive Swearing in Social Media (2020.lrec-1)
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| Challenge: | Swearing is a common form of verbal communication and occurs in social media and online forums . a study by a team of researchers has investigated the phenomenon of swearing in Twitter . |
| Approach: | They analyze tweets to determine abusive swearing using models that automatically predict it . they also investigate lexical, syntactic, and affective features that are more informative . |
| Outcome: | The proposed model can predict abusive swearing in a tweet context and provide an intrinsic evaluation of the model. |
Author Profiling for Abuse Detection (C18-1)
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| Challenge: | Existing methods for detecting abusive content rely on textual cues and lexical cue information. |
| Approach: | They propose a method that incorporates community-based profiling features of Twitter users to detect abusive content by using a dataset of 16k tweets. |
| Outcome: | The proposed approach outperforms the current state-of-the-art in abuse detection on a dataset of 16k tweets. |